Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Application of Artificial Intelligence in Sports
share announcement

Application of Artificial Intelligence in Sports

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 July 2019) | Viewed by 6115

Special Issue Editor

Prof. Dr. Arnold Baca

E-Mail Website
Guest Editor
Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6A, 1150 Wien, Austria
Interests: biomechanical research in sports; biomechanical modeling; human motion analysis; performance analysis; computer science in sport
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) commonly refers to the ability of computers or, more generally, of machines to perform tasks by imitating human intelligence. In order to do so, machines need to be able to learn, which may be accomplished by Machine Learning methods. Knowledge may thereby be generated from experience. In recent years, the integration of machine-aided intelligence into the design and implementation of innovative systems on the basis of state-of-the-art information and communication technologies has also become increasingly important for sports-related issues, often involving large date sets. There are a number of areas that AI now has an important role in and that makes use of Machine Learning methods. Fields of application include, among others, the analysis of sports performance, the assessment of injury risks, the provision of individual recommendations to athletes, the analysis of tactics in game sports, the prediction of performance development and competition outcomes, interactive communication with fans via chatbots, or the implementation of automated sports journalism. Modern sport information systems making use of wearables, GPS-devices, high-resolution cameras, and various sensors, for example, enable a prompt and automatic evaluation of sport-specific parameter values, thereby allowing the establishment of computer-based feedback and intervention routines. Local position measurement systems, as another example, are able to track players in game sports in almost real time and provide insights into physical loads and in tactical behavior, such as player creativity. The purpose of this Special Issue is to highlight the potential of the application of Artificial Intelligence in sports-related areas. Original papers may focus on various application scenarios, such as sports performance analysis, training and coaching, talent diagnosis, team composition, injury and fatigue prevention, intelligent sports equipment, computer vision in sports, sports data mining, sports journalism, marketing and sports betting.

Prof. Dr. Arnold Baca
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 977 KiB  
Article
Asymmetric Residual Neural Network for Accurate Human Activity Recognition
by Jun Long, Wuqing Sun, Zhan Yang and Osolo Ian Raymond
Information 2019, 10(6), 203; https://doi.org/10.3390/info10060203 - 6 Jun 2019
Cited by 35 | Viewed by 4848
Abstract
Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also [...] Read more.
Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sports)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop